The Myth of the Good Prehistoric Savage: The Origins of Social Differentiation and Complexity

An obvious consequence of this way of considering social organization as an emergent property of the mechanisms of cooperation (or the lack of it) and cultural transmission, is that the origins of social diversity, hierarchy and complexity can also be considered as emergent properties of relatively basic social mechanisms.

If for 99 % of its history humanity lived forming small scale, “egalitarian” communities, why those early undifferentiated groups diversified and coercion, power, inequality and hierachization have marked social evolution? First of all, what is “social complexity”? We should approach this term in the sense of a conceptual framework and not as a particular kind of society. The idea of com­plexity refers to phenomena with many parts and many possible arrangements of the relationships between those parts. Herbert Simon was one of the seminal thinkers in the study of complexity and also on computer simulation and artificial intelligence. In 1962, he put forward several key ideas: “(...) roughly, by a complex system I mean one made up of a large number of parts that interact in a no simple way. In such systems, the whole is more than the sum of the parts, not in an ultimate, metaphysical sense, but in the important pragmatic sense that, given the properties of the parts and the laws of their interaction, it is not a trivial matter to infer the properties of the whole. In the face of complexity, an in-principle reductionist may be at the same time a pragmatic holist. (Simon 1962: 468). Therefore, when we are speaking about social complexity we are speaking on the internal differentiation of subgroups of people within a well-defined group, and the existence of differentiated patterns of relationships or arrangement among those subgroups.

An important corollary to the very definition of complexity in social groups is that the behavior of the complex social system is difficult to predict because of the no simple interactions among the constituting social sub-groups. In a complex system we cannot provide a simple aggregation model of the system that adds up the independent behaviors of the parts; rather, the parts are influenced in their behaviors by the behaviors of other components. The state of the social system is fixed by the interdependent dynamics of agents and groups of agents; which implies that collective behavior can oscillate wildly with apparently similar initial conditions.

Cultural evolutionists usually speak of human societies evolving toward greater complexity or higher degrees of organization. This is an important aspect of any historical investigation. Is social inequality an immutable result of the destiny of any agglomeration of people, or the emergent consequence of the interaction between individuals and groups of individuals (households, communities, tribes, territories, etc.)? The increase in complexity in the course of social evolution that can be observed in historical terms is, however, neither inevitable nor universal. There is no reason to regard the evolutionary process as one of inevitable progress, nor is an increase in complexity of human societies inevitable (Nowotny 2005; Dundes and Harlow 2005; Anderson et al. 2014; Vanhee et al. 2014; Neumann and Secchi 2016).

If we equate social inequality with the formal definition of disorder, we would implement a computer model of social evolution in terms of an evolution from order—hunter gatherer intrinsic egalitarianism—to disorder—economic inequality, exploitation, social and political hierarchy and class struggle-. In physics, the terms order and disorder designate the presence or absence of some symmetry or corre­lation in a many-particle system. The strictest form of physical order in a solid is lattice periodicity: a certain pattern (the arrangement of atoms in a unit cell) is repeated again and again to form a translationally invariant tiling of space.

Nature tends from order to disorder in isolated systems. That means that “dis­order” is a more probable state of any system than order. Its measure is often called entropy (Kubat and Zeman 1975). The mathematical basis with respect to the association entropy has with order and disorder began, essentially, with the famous Boltzmann formula, S = k ln W, which relates entropy S to the number of possible states W in which a system can be found. In the case of social systems, the entropy of a collection of social agents within the system can be defined as a measure of their disorder or equivalently, how close a system is to equilibrium—that is, to perfect internal inequality. A more precise way to characterize social entropy is in terms of the different number of diverse arrangements along a given temporal trajectory. Thus, an increase in entropy means a greater number of microstates for the final state than for the initial state, and hence more possible arrangements of a system arrangement at any one instant. Here, a greater ‘dispersal of the total energy of a system' means the existence of many possibilities (Lambert 2002).

Annila and Salthe (2009), among many others (see also Tesfatsion 2003; Deguchi 2011), have regarded economic activity as an evolutionary process gov­erned by the 2nd law of thermodynamics.

Social structure is a common phenomenon in nature, and it is not a necessary characteristic of human “intelligence” or rationality. Specifically, many species of the order of primates show different patterns of “complex” social structure. The limits of collective action and the emergence of patterns of social affiliation and differentiation in primate societies have been recently simulated (Bryson et al. 2007; Puga-Gonzalez et al. 2009, 2014; De Vries 2009; Evers et al. 2011, 2012; King and Sueur 2011; Sueur et al. 2011; Dolado et al. 2014; Smith et al. 2016; Will 2016). For instance, Witkowski and Ikegami (2016) have created a virtual world in which agents progressively evolve the ability to use the information exchanged between each other via signaling to establish temporary leader-follower relations.

The basis for many of those studies is a model for the origins of domination in animal populations proposed by Hemelrijk (1999, 2002, 2004), Hemelrijk et al. (2005). In this virtual world, artificial entities live in a homogeneous world and only aggregate, and upon meeting one another and may perform dominance interactions in which the effects of winning and losing are self-reinforcing. Whether an agent will initiate an attack depends on the chance it has to defeat its opponent. If the risk of losing is large the likelihood that the agent will start an attack is small (‘risk-sensitive attack strategy'). When a dominance interaction actually takes place the outcome of the fight is decided probabilistically by the relative win chance. Defeating an opponent having a small probability to win increases the winner's dominance value less than defeating an opponent that has a large probability to win (‘damped positive feedback'). The ordering of the agents according to this hypo­thetical value can be read as the emergence of a ‘real hierarchy'. The agent's DOM value is intended to correspond with a real animal's capacity to win fights. By varying the intensity of aggression only, one may switch from egalitarian to des­potic virtual communities. In addition, artificial despotic communities show a clearer spatial centrality of dominants and, counter-intuitively, more rank overlap between the sexes than the egalitarian ones. Because of the correspondence with patterns in real animals, the model makes it worthwhile comparing despotic and egalitarian species for socio-spatial structure and rank overlap too. Furthermore, it presents with parsimonious hypotheses which can be tested for patterns of aggression, spatial structure and the distribution of social positive and sexual behavior.

To sum up, violence and revenge may reduce the survival probability of the population. Flight from known aggressors enhanced the survival of the total pop­ulation, at the expense of social cohesion. These examples show the possible role of violence and aggression on the evolution towards increasing social complexity (see also Ilachinski 2004; Taylor et al. 2004; Clements and Hughes 2004; Younger 2005, 2011; Lim et al. 2007; Philips et al. 2014).

Rather than an unconscious solution to instinctive violence, we may suggest that it was the rational and conscious emergence of social norms constraining free will what characterized social evolution and the development of more complex social systems (Savarimuthu et al. 2011a). It has been suggested that social norms help people self-organizing in many situations without relying on a centralized and omnipresent authority (Villatoro and Sabater-Mir 2009; De la Cruz et al. 2012; Vila et al. 2013. See also Makowsky and Smaldino 2015; Roos et al. 2015; Gelfand and Jackson 2016; Horiuchi 2015; Santos et al. 2016; Thurmel 2016). On the contrary to institutional rules, the responsibility to enforce social norms is not the task of a central authority but a task of each member of the society (Ghorbani and Bravo 2016).

Castelfranchi et al. (1998) suggested the need of a social norm prescribing: “attack an eater unless the food item being eaten is marked as ‘owned’ by that agent”. The multi-agent system is composed out of two different sub-populations: agents either respect the finder-keeper precept (the Respectful) or not (the Chea­ters). Either through experience or through communications the agents learn whether another agent is a Respectful or a Cheater. The ‘normative’ algorithm of the Respectful is modified so that they respect the norm only with agents known to be Respectful. This looks like a sanction towards the Cheaters, but as the finder-keeper precept does not hold for any Cheater—it is in fact not prescribed for them—they cannot violate it, and therefore they cannot be sanctioned. The Cheaters are defined as non-normative, i.e., self-interested agents. In this respect, in the Castelfranchi, Conte and Paolucci model, it is the Respectful who violate the finder-keeper norm if they do not respect the Cheaters. It is rational that the Respectful only respect themselves, but how do we know, that decisions about the respect or disrespect of norms are the result of a rational calculus? Is it rational that the Cheaters always disrespect the finder-keeper norm? Under the title of deviant behavior, there is a long research tradition in sociology that investigates the reasons for a lack of respect of norms, which could advance theory construction here. Working on the results of this model, Saam and Harrer (1999) have studied the hypothesis, which can be traced back to Marx, stating that the “finder-keeper” norm while controlling aggression efficaciously reduces social inequality holds only in quite egalitarian societies. Throughout a variety of non-egalitarian societies, it instead increases social inequality. The authors have remodeled the model's nor­mative behavior from a sociological point of view by implementing Haferkamp's theory of action approach to deviant behavior, demonstrating that it is possible to integrate power into computational models of norms.

Gavrilets (2012) shows that the differences in fighting abilities lead to the emergence of hierarchies where stronger individuals take away resources from weaker individuals and, as a result, have higher reproductive success. He has shown that the logic of within-group competition implies under rather general conditions that each individual benefits if the transfer of the resource from a weaker group member to a stronger one is prevented. This effect is especially strong in small groups. This effect can result in the evolution of a particular, genetically controlled psychology causing individuals to interfere in a bully-victim conflict on the side of the victim. A necessary condition is a high efficiency of coalitions in conflicts against the bullies.

Ray and Liew (2003) adopt a different approach by assuming that leaders are the better performing individuals that help others in the society to improve through an intrasociety information exchange. “Better” may refer to different behaviors: more successful in hunting or sharing resources with others, more efficient in fighting against violence and aggression, etc. Such “leaders” would improve only through an intersociety information exchange that results in the migration of a leader from a society to another that is headed by better performing leaders. This process of leader migration would halve helped the “better” performing societies to expand and survive where others disaggregate and disappear.

Hazy (2008) defines leadership as those aspects of agent interactions which catalyze changes to the local rules defining other agents' interactions. According to this author, there are five distinct aspects of leadership to be observed. Leadership involves actions among agents that: (a) identify or espouse a cooperation strategy or program, (b) catalyze conditions where other agents choose to participate in the program, (c) organize choices and actions in other agents to navigate complexity and avoid interaction catastrophe (sometimes called “complexity catastrophe”), (d) form a distinct output layer that expresses the system as a unity in its envi­ronment, and (e) translate feedback into structural changes in the influence network among agents. Leadership in all of its aspects serves three functional demands in supporting the purposes of participating agents and groups of agents. Generative leadership identifies and generates variety in the programs of action, resources and capabilities available to the community. Convergent leadership increases the per­ceived benefit to cost ratio of participating in a program of action; this deepens and makes less rugged the attractor basin associated with agents choosing to adopt a particular program of action. Unifying leadership promotes collective identity, or “unity,” and catalyzes actions and communications that pressure others to conform to a program; it clarifies boundaries and enables increased participation and cooperation at the margin within an attractor basin. See also Boal and Schultz (2007) about this view on “strategic” leadership.

Accepting dominance and creating institutionalized forms of leadership has been considered as an answer to conflict (Spisak et al. 2011). Particular attention has been given to the role of the “follower” and the specific pressures encouraging “followership investment” and the emergence of traits intended to signal potential leadership ability. This is a source of internal differentiation, and hence of “com­plexity” as leaders differentiate from the rest of the population. Eguiluz et al. (2005) have created a virtual world in which leaders are individuals getting a large payoff who are imitated by a considerable fraction of the population, conformists are unsatisfied cooperative agents that keep cooperating, and exploiters are defectors with a payoff larger than the average one obtained by cooperators. The dynamics generate a social network that can have the topology of a small world network. The network has a strong hierarchical structure in which the leaders play an essential role in sustaining a highly cooperative stable regime. But disruptions affecting leaders produce social crises described as dynamical cascades that propagate through the network. “Prestige” increases the different nature of leaders, coming into existence to signal the level of skill held by their owners, in order to gain deference benefits from learning individuals in exchange for access.

Clemson and Evans (2012) have simulated a virtual world in which agents can choose to follow the choices made by a neighbouring agent in a social network (the future “leader”). The authors investigated three different types of possible social network (the Erdos-Renyi random graph, the scale-free network, and the regular-ring network), chosen to represent various extremes in terms of their sub­strate degree distributions, and investigated each using a variety of network sizes. Results highlight the apparently universal aspects of social behaviour. This uni­versal form shows that, irrespective of the type of underlying social network, the leadership structure which emerges has an initial power-law section. That is, there are always a few agents whose actions are copied by many others. However, the authors have also found that the nature of the social network linking agents does have a significant effect on the ‘fatness’ of the leadership distribution. The virtual abstract worlds investigated here are clearly not realistic in many senses, but they can capture some of the basic features of competition dynamics thorough history. It is clear that the copying of strategies from a local social neighbourhood does lead to the emergence of a leadership structure, regardless of the nature of the social network.

Among the agent-based models that explicitly take into account social inequality and conflict, we can mention Smith and Choi (2007), who have simulated the emergence of inequality in small-scale societies. The model is predicated on the assumption that a limited number of asymmetries, such as differential control over productive resources, can explain the emergence of institutionalized inequality. They also draw on contemporary evolutionary theory in order to avoid the pitfalls of naive functionalism and teleology. Their approach is not to deny any possibility of collectively beneficial outcomes or directionality to sociopolitical evolution, but rather to show how it emerges from the interaction of individual agency, social structure, and environmental constraints. In their computer simulation, some agents (depicted as “Patrons”) control limited areas with greater per capita resource endowments, and can trade access to these for services from less fortunate agents (depicted as “Clients”). There is also an additional set of isolated agents which simply defend richer patches for their exclusive use, while others (depicted as “Doves”) share any resources on their patch with other non-territorial agents (Doves or Clients). In the initial simulation, all agents are Doves, randomly distributed over a heterogeneous environment, so each agent has different probabilities to become a Patron or a Client depending on its behavior and the productivity of the area it is placed. Under default parameter values, non-territorial strategies dominate, split equally between Dove and Client types, and isolated and Patron types are about equally represented in the remaining areas. However, a stable patron-client regime emerges in about one third of all runs, and takes over the population about 10 percent of the time. Obviously, environmental heterogeneity is critical, as Patrons capitalize on their relatively rich patch endowments to participate in exchanges with Clients, and hence variation in property endowment, provides the initial opportunity for the emergence of inequality. Yet this is not sufficient, nor can this be glossed as “environmental determinism”, since alternative strategies, interacting with similar resource heterogeneity do not generate socioeconomic inequality. Demographic parameters have also a strong effect on the relative success of territorial and non-territorial strategies. When mortality is high or reproductive rate low, the initial (all-Dove) population expands slowly so that isolated and Patron agents are able to spread and control rich patches, effectively keeping Dove and Client numbers low at equilibrium. Conversely, low mortality or high reproductive rate allows Doves to proliferate rapidly, and territorial agents are locked out (with Clients arising in modest numbers through mutation and drift). Increased mutation rates are favorable to the spread of Client and Patron strategies, but only because this retards the initial proliferation of Doves.

Although this model may be considered as too restricted and limited, it allows exploring the hypothesis that a limited number of asymmetries can explain most cases of emergence of institutionalized inequality through history, specially in ancient times. These might include asymmetries in control over productive resources, control over external trade, differential military ability (and resultant booty and slaves), or control of socially significant information. As simulations suggest, these asymmetries need not be employed coercively, as long as they are economically defensible and can provide an advantage in bargaining power suffi­cient to allow the concentration of wealth and/or power in the hands of a segment of the social group or polity. The modeling indicates that such asymmetries can be self-reinforcing, and thus quite stable to moderate perturbations over time. Because most of the social transactions based on them are mutual rather than coercive, it can be suggested that such systems are likely to be more stable than the stratified social systems (e.g., nation states) that eventually succeed them.

Dwight Read (2002, 2003) has followed a very similar approach and shows how competition is shown to play a critical role in the way interaction—among decision-making, demographic parameters, and social units that organize resource ownership and procurement—either promotes or inhibits change in social organization.

Koykka and Wild (2015) have simulated how group dispersal may be initiated by leaders. The authors use a theory of inclusive fitness to examine the incentives for leading and following in this context. High relatedness, significant reductions in the cost of dispersal due to dispersing in groups, and reproductive skew in favor of followers facilitates the emergence of group dispersal. In contrast to some previous theoretical work, which has either concluded that leadership is uniformly altruistic or that it is uniformly selfish, this investigation suggests that at evolutionary equilibrium the incentives for leading can be either selfish or altruistic. The nature of result (selfish or altruistic) depends on ecological and social conditions such as the cost of dispersal and the relatedness between leaders and followers. The model demonstrates that kin selection is sufficient and that individual differences in con­dition and ability are not necessary to promote the emergence and maintenance of leader-follower relationships. It has been suggested (Layton et al. 2012) that band formation evolved in humans from the more transient fissioning behavior as a solution to the conflicting pressures of sustaining higher levels of cooperation required in hunting and the division of labor in a more dispersed community. If disputes break out, or if resources in the band territory are temporarily depleted, the existence of a wider community continues to be adaptive. Van der Post et al. (2015) have studied the evolutionary progression from “leader-follower” societies to “fission-fusion” societies, where cooperative vigilance in groups is maintained via a balance between within- and between-group selections. Group-level selection can be seen as generated from an assortment that arises spontaneously when vigilant and non-vigilant agents have different grouping tendencies. The evolutionary maintenance of small groups, and cooperative vigilance in those groups, is therefore achieved simultaneously. The evolutionary phases, and the transitions between them, depend strongly on behavioral mechanisms.

An obvious consequence of the emergence of leader-follower dominance rela­tionships is the signaling of this difference, that is, the origins of prestige as a way to express hierarchical difference. Plourde (2008) argues that strategies towards sig­naling social difference can invade a non-signaling population and can be evolu- tionarily stable under a set of reasonable parameter values. Increasing competition levels can be likely the selective force driving the adoption of this novel strategy. Two changes in the social context in which prestige processes operate have been also tentatively identified as leading to increased levels of competition for prestige: (1) increasing group sizes and (2) increasing complexity or size of the existing cultural repertoire (see also Reyes-Garcia et al. 2008; Heinrich 2009; Bentley et al. 2011; Halevy et al. 2012; Cheng et al. 2013). Similar approaches are being con­sidered for analyzing social evolution from egalitarian human communities to “complex” and internally diversified human groups with complex interaction links between groups hierarchically organized (Pauketat 1996; Cohen 1998; Walby 2007; Heinrich and Boyd 2008; Helbing 2012; Hoffrage and Hertwig 2012; Edmonds and Meyer 2013; Hofstede 2013; Perch et al. 2013; Gavrilets and Fortunato 2014; von Rueden 2014; Skyrms 2014).

But conflict, violence and domination are not the only sources of social diver­sification and the increase of social entropy in small scale societies with scarce development of their means of production (hunter-gatherer societies). Empirical evidence suggests that division of labor in animal societies is positively related to group size. Frolova and Korobitzin (2002), Robinson-Cox et al. (2007) and Dyble et al. (2015) have simulated the emergence of gender stratification in artificial societies of hunter-gatherers. Jeanson et al. (2007) have simulated how group size influences division of labor using a fixed response-threshold model. They have investigated how expected by-products of increased population size, including demand (total work need relative to total work force available) and task number, affect this relationship. Their results indicate that both low demand and high task number positively influence division of labor. If social division of labor is an emergent property of group size and the need of increasing productivity, Bentley et al. (2005) have explored how an exchange network coevolves with the changing specializations of the agents within it. Through simulation, the authors keep track of who is connected to whom through a mapping of the network and the specializa­tions of each agent, and they test the effects of simplified individual motivations for exchange, the make-up of the initial population of agents, and abstract represen­tations of basic ideological dispositions such as the belief in private ownership. The aim was to test whether specialization and wealth inequalities are natural, self-organizing qualities of a small-scale economy. Internal differentiation can emerge, even in the absence of conflict, violence and the needs of protection (see also Crabtree 2015). Chiang (2015) argues that the way inequality evolves as a result of egalitarian sharing is determined by the structure of “who gives whom”: social networks make a difference in how egalitarian sharing influences the evo­lution of inequality.

Social structure is an emergent property of a group of individuals; it cannot be the property of any single agent. Explanations for the evolution of complex soci­eties assume that the organizational benefits of complexity are the reason it evolves (Edmonds et al. 2009). Among others, Rosenberg (2009) proposes that complexity is a product of group selection. He suggests that the organizational benefits are exaltations, built on authority only after it already exists, and which first develops to provide a more primitive benefit, conflict resolution. He further argues that in contexts where the maintenance of group unity confers a net top-down advantage to an egalitarian group's members, even after factoring in loss of personal autonomy, egalitarian ideology will be abandoned and replaced by hierarchical ones.

It is obvious that much more research is needed in this area, not only as developments of abstract social theory, but computer simulations calibrated with historical data in well-defined scenarios (see later, Sect. 1.2.6).

1.2.6